A multi-state coarse grained modeling approach for an intrinsically disordered peptide.

Area of Science:

• Biophysics
• Computational Biology
• Protein Dynamics

Background:

• Proteins with marginally stable structures are sensitive to external stimuli.
• Coarse-grained (CG) models often struggle to predict the conformational space of intrinsically disordered peptides.

Purpose of the Study:

• To develop a multi-state CG model capable of capturing the conformational behavior of intrinsically disordered peptides in various environments.
• To validate the model's accuracy against all-atom simulations and experimental data.

Main Methods:

• A bottom-up approach was used, combining a generic base model with nonbonded interactions (hydrogen bonds, electrostatics, hydrophobic forces).
• All-atom potential of mean force calculations were employed to parameterize the CG model for three distinct peptide states.
• The LKα14 peptide was used as a test system.

Main Results:

• The developed CG model accurately reproduced the intrinsically disordered state of LKα14 in dilute solutions.
• The model predicted α-helix formation upon aggregation or interface contact, consistent with experimental observations.
• The CG model successfully simulated tetrameric unit stability and conformational changes.

Conclusions:

• The bottom-up CG modeling approach provides a balanced representation of nonbonded interactions for peptides.
• This multi-state CG model is effective for predicting the conformational dynamics of intrinsically disordered peptides in diverse environments.
• The model's success validates its potential for broader applications in protein structure and function studies.